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    <head rend="h3">Llama 3.1 Acceptable Use Policy</head>
    <p>Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.1. If you access or
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      found at https://llama.meta.com/llama3_1/use-policy</p>

    <head rend="h4">Prohibited Uses</head>
    <p>We want everyone to use Llama 3.1 safely and responsibly. You agree you will not use, or allow others to use,
      Llama 3.1 to:</p>
    <list rend="ol">
      <item>Violate the law or others’ rights, including to:<list rend="ol">
          <item>Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful
            activity or content, such as:<list rend="ol">
              <item>Violence or terrorism</item>
              <item>Exploitation or harm to children, including the solicitation, creation, acquisition, or
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        <lb />Please report any violation of this Policy, software “bug,” or other problems that could lead to a
        violation of this Policy through one of the following means:<list rend="ul">
          <item>Reporting issues with the model: https://github.com/meta-llama/llama-models/issues</item>
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    <p>Log in or Sign Up to review the conditions and access this model content.</p>

    <head rend="h2">Model Information</head>
    <p>The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and
      instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned
      text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the
      available open source and closed chat models on common industry benchmarks.</p>
    <p>Model developer: Meta</p>
    <p>Model Architecture: Llama 3.1 is an auto-regressive language model that uses an optimized transformer
      architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback
      (RLHF) to align with human preferences for helpfulness and safety.</p>
    <p>Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.</p>
    <p>Llama 3.1 family of models. Token counts refer to pretraining data only. All model versions use Grouped-Query
      Attention (GQA) for improved inference scalability.</p>
    <p>Model Release Date: July 23, 2024.</p>
    <p>Status: This is a static model trained on an offline dataset. Future versions of the tuned models will be
      released as we improve model safety with community feedback.</p>
    <p>License: A custom commercial license, the Llama 3.1 Community License, is available at:
      https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE</p>
    <p>Where to send questions or comments about the model Instructions on how to provide feedback or comments on the
      model can be found in the model README. For more technical information about generation parameters and recipes for
      how to use Llama 3.1 in applications, please go here.</p>

    <head rend="h2">Intended Use</head>
    <p>Intended Use Cases Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned
      text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of
      natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the
      outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1
      Community License allows for these use cases.</p>
    <p>Out-of-scope Use in any manner that violates applicable laws or regulations (including trade compliance laws).
      Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in
      languages beyond those explicitly referenced as supported in this model card**.</p>
    <p>**Note: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages.
      Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with
      the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that
      any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.</p>

    <head rend="h2">How to use</head>
    <p>This repository contains two versions of Meta-Llama-3.1-8B-Instruct, for use with transformers and with the
      original <code>llama</code> codebase.</p>

    <head rend="h3">Use with transformers</head>
    <p>Starting with <code>transformers &gt;= 4.43.0</code> onward, you can run conversational inference using the
      Transformers <code>pipeline</code> abstraction or by leveraging the Auto classes with the <code>generate()</code>
      function.</p>
    <p>Make sure to update your transformers installation via <code>pip install --upgrade transformers</code>.</p><code>import transformers
import torch

model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

outputs = pipeline(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
</code>
    <p>Note: You can also find detailed recipes on how to use the model locally, with <code>torch.compile()</code>,
      assisted generations, quantised and more at <code>huggingface-llama-recipes</code></p>

    <head rend="h3">Tool use with transformers</head>
    <p>LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting here.</p>
    <p>Tool use is also supported through chat templates in Transformers. Here is a quick example showing a single
      simple tool:</p><code># First, define a tool
def get_current_temperature(location: str) -&gt; float:
    """
    Get the current temperature at a location.
    
    Args:
        location: The location to get the temperature for, in the format "City, Country"
    Returns:
        The current temperature at the specified location in the specified units, as a float.
    """
    return 22.  # A real function should probably actually get the temperature!

# Next, create a chat and apply the chat template
messages = [
  {"role": "system", "content": "You are a bot that responds to weather queries."},
  {"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
]

inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
</code>
    <p>You can then generate text from this input as normal. If the model generates a tool call, you should add it to
      the chat like so:</p><code>tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
</code>
    <p>and then call the tool and append the result, with the <code>tool</code> role, like so:</p><code>messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
</code>
    <p>After that, you can <code>generate()</code> again to let the model use the tool result in the chat. Note that
      this was a very brief introduction to tool calling - for more information,
      see the LLaMA prompt format docs and the Transformers tool use documentation.</p>

    <head rend="h3"> Use with <code>llama</code>

    </head>
    <p>Please, follow the instructions in the repository</p>
    <p>To download Original checkpoints, see the example command below leveraging <code>huggingface-cli</code>:</p>
    <code>huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/*" --local-dir Meta-Llama-3.1-8B-Instruct
</code>

    <head rend="h2">Hardware and Software</head>
    <p>Training Factors We used custom training libraries, Meta's custom built GPU cluster, and production
      infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production
      infrastructure.</p>
    <p>Training utilized a cumulative of 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per
      the table below. Training time is the total GPU time required for training each model and power consumption is the
      peak power capacity per GPU device used, adjusted for power usage efficiency.</p>
    <p>Training Greenhouse Gas Emissions Estimated total location-based greenhouse gas emissions were 11,390 tons CO2eq
      for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and
      matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas
      emissions for training were 0 tons CO2eq.</p>
    <p>The methodology used to determine training energy use and greenhouse gas emissions can be found here. Since Meta
      is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by
      others.</p>

    <head rend="h2">Training Data</head>
    <p>Overview: Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The
      fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated
      examples.</p>
    <p>Data Freshness: The pretraining data has a cutoff of December 2023.</p>

    <head rend="h2">Benchmark scores</head>
    <p>In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the
      evaluations, we use our internal evaluations library.</p>

    <head rend="h3">Base pretrained models</head>

    <head rend="h3">Instruction tuned models</head>

    <head rend="h4">Multilingual benchmarks</head>

    <head rend="h2">Responsibility &amp; Safety</head>
    <p>As part of our Responsible release approach, we followed a three-pronged strategy to managing trust &amp; safety
      risks:</p>
    <list rend="ul">
      <item>Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use
        cases supported by Llama.</item>
      <item>Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
      </item>
      <item>Provide protections for the community to help prevent the misuse of our models.</item>
    </list>

    <head rend="h3">Responsible deployment</head>
    <p>Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama
      models have been responsibly deployed can be found in our Community Stories webpage. Our approach is to build the
      most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the
      generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for
      their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama
      systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer
      to the Responsible Use Guide to learn more.</p>

    <head rend="h4">Llama 3.1 instruct</head>
    <p>Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable
      resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available,
      safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For
      more details on the safety mitigations implemented please read the Llama 3 paper.</p>
    <p>Fine-tuning data</p>
    <p>We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with
      synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based
      classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality
      control.</p>
    <p>Refusals and Tone</p>
    <p>Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well
      as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our
      safety data responses to follow tone guidelines.</p>

    <head rend="h4">Llama 3.1 systems</head>
    <p>Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be
      deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to
      deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety
      alignment as well as mitigating safety and security risks inherent to the system and any integration of the model
      or system with external tools.</p>
    <p>As part of our responsible release approach, we provide the community with safeguards that developers should
      deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our reference
      implementations demos contain these safeguards by default so developers can benefit from system-level safety
      out-of-the-box.</p>

    <head rend="h4">New capabilities</head>
    <p>Note that this release introduces new capabilities, including a longer context window, multilingual inputs and
      outputs and possible integrations by developers with third party tools. Building with these new capabilities
      requires specific considerations in addition to the best practices that generally apply across all Generative AI
      use cases.</p>
    <p>Tool-use: Just like in standard software development, developers are responsible for the integration of the LLM
      with the tools and services of their choice. They should define a clear policy for their use case and assess the
      integrity of the third party services they use to be aware of the safety and security limitations when using this
      capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party
      safeguards.</p>
    <p>Multilinguality: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian,
      Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet
      performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to
      converse in non-supported languages without implementing finetuning and system controls in alignment with their
      policies and the best practices shared in the Responsible Use Guide.</p>

    <head rend="h3">Evaluations</head>
    <p>We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations
      measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool
      calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and
      Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and
      we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also
      available if relevant to the application.</p>
    <p>Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were
      crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.</p>
    <p>Red teaming</p>
    <p>For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via
      adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets.</p>
    <p>We partnered early with subject-matter experts in critical risk areas to understand the nature of these
      real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we
      derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information
      or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in
      cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content
      specialists with background in integrity issues in specific geographic markets.</p>

    <head rend="h3">Critical and other risks</head>
    <p>We specifically focused our efforts on mitigating the following critical risk areas:</p>
    <p>1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness</p>
    <p>To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed
      to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan
      or carry out attacks using these types of weapons.</p>
    <p>2. Child Safety</p>
    <p>Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce
      outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via
      fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks
      through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based
      methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3
      is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially
      violating content while taking account of market specific nuances or experiences.</p>
    <p>3. Cyber attack enablement</p>
    <p>Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in
      terms of skill level and speed.</p>
    <p>Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in
      cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from
      previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these
      models could effectively function as independent agents in executing complex cyber-attacks without human
      intervention.</p>
    <p>Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the
      effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1
      Cyber security whitepaper to learn more.</p>

    <head rend="h3">Community</head>
    <p>Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to
      accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI
      and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to
      adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on
      safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely
      distributed across ecosystem partners including cloud service providers. We encourage community contributions to
      our Github repository.</p>
    <p>We also set up the Llama Impact Grants program to identify and support the most compelling applications of Meta’s
      Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists
      from the hundreds of applications can be found here.</p>
    <p>Finally, we put in place a set of resources including an output reporting mechanism and bug bounty program to
      continuously improve the Llama technology with the help of the community.</p>

    <head rend="h2">Ethical Considerations and Limitations</head>
    <p>The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to
      work for a wide range of use cases. It is thus designed to be accessible to people across many different
      backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without
      insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may
      appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of
      all users, especially in terms of the values of free thought and expression that power innovation and progress.
    </p>
    <p>But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing
      conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama
      3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate,
      biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1
      models, developers should perform safety testing and tuning tailored to their specific applications of the model.
      Please refer to available resources including our Responsible Use Guide, Trust and Safety solutions, and other
      resources to learn more about responsible development.</p>
    <list rend="dl">
      <item rend="dt-1">Downloads last month</item>
      <item rend="dd-1">3,163,293</item>
    </list>

    <head rend="h2">Model tree for meta-llama/Llama-3.1-8B-Instruct</head>
    <p>Base model</p>meta-llama/Llama-3.1-8B
  </main>
  <comments />
</doc>